132 results about "Sensor model" patented technology

The invention discloses a pipe network operation and maintenance management system based on data analysis, which relates to the technical field of municipal infrastructure operation and maintenance management and comprises an equipment feature modeling module, a group anomaly identification module, a common-mode risk tracing module, a dynamic risk modeling module, a regulation and control strategy optimization module and a closed-loop self-adaptive updating module. Based on historical operation data and real-time acquisition parameters, feature coding is performed on the model, batch, position and time sequence of the sensor, and an equipment attribute mapping matrix is constructed. According to the method, the equipment attribute mapping matrix and the group anomaly identification mechanism are constructed, and external induction factor positioning and the dynamic risk weight map are combined, so that intelligent adjustment of regulation and control parameters and anomaly interference avoidance are realized, a closed-loop optimization process is constructed, and the identification accuracy, the control safety and the operation toughness of a pipe network system in a multi-disturbance scene are improved.

The invention provides a map construction method and system based on laser vision dynamic weighted fusion, and belongs to the technical field of data processing, and the method comprises the steps: obtaining an RGB image and a depth image through a vision sensor, and obtaining point cloud data through a laser radar; aligning the depth image with the RGB image, and removing invalid depth pixels; based on an ORB-SLAM2 algorithm, performing visual SLAM, extracting features of the depth image and the RGB image, and outputting sparse visual point cloud data; based on a Gmapping algorithm, performing laser radar SLAM, and outputting a 2D occupied grid map; projecting the sparse visual point cloud data into a 2D occupation grid map, and calculating the semantic occupation probability of each grid; calculating the geometric occupancy probability of each grid through an anti-sensor model; performing weighted fusion on the semantic occupancy probability and the geometric occupancy probability, and calculating a fusion occupancy probability; and generating a fusion map according to the fusion occupation probability.

The invention provides a dynamic fault-tolerant control method based on an expected function safety mechanism, and the method comprises the steps: 1, selecting the model of a sensor, and enabling the sensor to be distributed at the periphery of a vehicle body; 2, building a decision algorithm model, carrying out environment scene recognition, planning a driving path and speed according to data obtained by a sensing layer, evaluating safety risks of different decisions, and carrying out judgment and decision making on various conditions; 3, control interfaces of a vehicle power system, a steering system and a braking system are established, and the electronic control unit converts a decision control instruction output by the decision algorithm model into actual vehicle operation through the control interfaces; 4, performing an expected function safety mechanism, performing real-time monitoring on state parameters of the vehicle, and analyzing detection data through a fault detection algorithm; 5, implementing a fault tolerance strategy; and 6, continuously monitoring the actual driving state and the system response condition of the vehicle, and adjusting the fault tolerance strategy.

The invention discloses a fault detection method and a fault detection device for an accelerometer of a downhole tool. Taking an accelerometer and a gyroscope as state quantities, establishing a generalized nonlinear system for the downhole tool sensor model, and performing linearization processing to obtain a generalized linear variable parameter system; based on a T-N-L observer architecture, constructing a system state estimation error equation and a residual error generation equation, establishing a fault sensitivity and interference robustness evaluation system on the basis of the system state estimation error equation and the residual error generation equation, and determining observer parameters; and the sensor obtains a system state value, an estimated value is obtained through the observer, a residual value of a current system state quantity is calculated to judge an occurrence event, and a threshold value of sensor fault occurrence under the current event is calculated to judge a fault occurrence condition. A nonlinear model is adopted, the system dynamic state is described more accurately, the model is linearized, it is guaranteed that the model and the actual working condition have the high matching degree, and meanwhile a corresponding linear variable parameter system is obtained to reduce the calculated amount.

Satellite images have inherent geo-positional errors of orders a few meters. Corrections are achieved by adjusting a sensor model which maps ground coordinates of control features into image coordinates and establishing a correspondence between the ground and image features, in this case a road network. The ground coordinates are obtained from mobile pose points. To adjust the sensor model we rely on the fact that the roads are typically much more uniform than surrounding features, and therefore have smaller entropy. The sensor model is adjusted so that the image pixels, obtained from projecting ground coordinates of the mobile pose points onto the image, minimize the entropy of the pixels that represent the road network.

A system and method for placing and configuring physical security sensors & barriers, are described. The system generates and analyzes a three-dimensional model of a customer's premises using non-imaging sensors to determine optimal sensor placement. Based on customer requirements, the system distributes sensor models within the premises while accounting for environmental obstructions, coverage gaps, and redundancy minimization. An extended reality (XR) interface allows users to visualize and refine sensor configurations before deployment. The system integrates AI-driven analytics to optimize surveillance coverage, adjust sensor orientations dynamically, and generate detailed reports outlining sensor locations and configurations. The sensor stand transmits real-time data to the system for continuous monitoring, predictive maintenance, and security threat analysis. The system enhances security planning by reducing design time, installation costs, preventing post-deployment modifications, and ensuring comprehensive monitoring coverage across diverse environments, including commercial, industrial, and critical infrastructure sites.

The invention discloses a multi-robot cooperative positioning and elastic formation control method based on a distributed Kalman consistency filtering (DKCF) algorithm. Aiming at the problems of insufficient sensor noise suppression, poor robustness under network attack and the like in the prior art, a dynamic model containing process noise and a sensor model of measurement noise are constructed, and a consistency state estimation method of weighted fusion observation data is provided. According to the method, multi-modal redundant sensor information is fused, and an improved DKCF is combined, so that cooperative state estimation between robots is realized, and the positioning precision in a noise environment is effectively improved; an elastic positioning framework under denial of service attack is established, and the robustness to network attack is enhanced while the formation precision is ensured through the joint design of distributed cooperative position estimation and a formation controller. According to the method, high positioning precision and a stable formation form can still be kept in the face of network attacks of denial of service, and the safety cooperation performance of a multi-robot system in a complex scene is remarkably improved.

This invention provides a method for modeling the starting performance of a marine gas turbine. First, a gas turbine aero-thermodynamic performance calculation model is established using C++, and a heat transfer model, temperature sensor model, and control law model for the gas turbine's high-temperature components are established using the M language. Then, based on test data from the gas turbine startup process, the method analyzes how the pressure ratio and efficiency of typical gas turbine components change with the physical speed of the gas generator during startup. The corresponding low-speed characteristics of the gas turbine's rotating components are generated and corrected, enabling high-precision, real-time simulation of the gas turbine's starting performance. This method considers the heat exchange between the high-temperature fuel gas and the ambient atmosphere and the gas turbine itself, improving the accuracy of the gas turbine startup performance simulation. The method can provide a simulation testing tool for the design and optimization of gas turbine startup control laws.

A sensor for detecting hazards is described that includes a memory and a processor. The processor may be configured to generate sensor profile data associated with a location and apply the sensor profile data to a sensor model profile associated with the location wherein the sensor model profile includes a plurality of parameter levels for the location generated by a machine learning model. The processor may also be configured to identify a discrepancy between the sensor profile data and the sensor model profile and determine a potential hazard at the location based upon the discrepancy between the sensor profile data and the sensor model profile. The processor may further be configured to generate an alert based upon the potential hazard at the location.

Model selection is disclosed. Features used as inputs to models are scored in terms of importance and health. The importance and health scores are combined in order to generate a model score for each model. The model with a score above a threshold score is selected and deployed.

The invention provides an automatic driving live-action simulator based on three-dimensional Gaussian sputtering, and the simulator comprises a general model interface module which achieves the integration of different Gaussian models, sensor models and vehicle kinematics models through providing a standardized interface, and obtains basic elements and self-vehicle elements in a simulation scene; the internal scene representation module is used for defining and organizing the basic elements in a structured mode to obtain environment elements; the collision detection module is used for distinguishing foreground objects and backgrounds of the environment elements, respectively carrying out foreground collision detection and background collision detection, and carrying out collision judgment on the self-vehicle elements and the environment elements in the simulation scene; and the simulation control module is used for performing dynamic response of the simulator to the external control signal based on the internal scene representation module and the collision detection module. The method can meet the requirements of the automatic driving algorithm for massive high-quality data, and is suitable for training and testing of the automatic driving algorithm.

The present application discloses a method for intelligent escort of a high-value spacecraft cluster, belonging to the technical field of spacecraft, including: obtaining the initial data of the spacecraft cluster, where the spacecraft cluster includes multiple spacecraft with autonomous decision-making; according to the initial data, combining the spacecraft orbit model, satellite on-board sensor model, and illumination model to conduct environmental deduction on the spacecraft cluster and obtain the current simulated environment; constructing a behavior rule library and a capability rule library for the spacecraft cluster; setting a reward function, establishing a reinforcement learning model according to the multi-agent reinforcement learning algorithm and the reward function, and iteratively training the reinforcement learning model according to the current simulated environment, behavior rule library, and capability rule library until the reinforcement learning model converges; simulating the tasks in the real scenario according to the converged reinforcement learning model to obtain the final multi-agent decision result and performing visualization processing. Making decisions for the problem of spacecraft cluster approaching can avoid the radiation or interference of other spacecraft.

A system and method for placing and configuring physical security sensors & barriers, are described. The system generates and analyzes a three-dimensional model of a customer's premises using non-imaging sensors to determine optimal sensor placement. Based on customer requirements, the system distributes sensor models within the premises while accounting for environmental obstructions, coverage gaps, and redundancy minimization. An extended reality (XR) interface allows users to visualize and refine sensor configurations before deployment. The system integrates AI-driven analytics to optimize surveillance coverage, adjust sensor orientations dynamically, and generate detailed reports outlining sensor locations and configurations. The sensor stand transmits real-time data to the system for continuous monitoring, predictive maintenance, and security threat analysis. The system enhances security planning by reducing design time, installation costs, preventing post-deployment modifications, and ensuring comprehensive monitoring coverage across diverse environments, including commercial, industrial, and critical infrastructure sites.

The application discloses a kind of HIL simulation methods of underground mining whole process, it is related to the field of autonomous driving simulation test, including: the point cloud data of underground scene is collected, and CARLA format simulation scene is generated;The generated CARLA format simulation scene is compiled and imported by Unreal engine, vehicle model and laser radar sensor model are built in Carla simulator, and the point cloud data packet of simulation scene is collected;According to the point cloud data packet, mapping is carried out by SLAM algorithm, and the point cloud map of PCD format is generated;According to the PCD point cloud map, the map of simulation scene is constructed;Hardware-in-the-loop HIL simulation platform including host computer, lower computer and actuating mechanism is built;The map of simulation scene constructed is loaded to the hardware-in-the-loop HIL simulation platform constructed, and the real mine working condition scene is simulated through simulation platform, and the mine working condition unmanned driving algorithm is verified.The present application improves the coverage of test by whole process closed loop etc., for the difficulty of comprehensive evaluation unmanned driving test in prior art.

The invention provides a fluid control device, a control unit for the fluid control device, a fluid control method, and a storage medium, which can realize high-speed response and high-precision flow rate control, and an observer determines the flow rate of the fluid based on the deviation between a flow rate measurement value output by a flow rate sensor and a flow rate estimation value output by a flow rate sensor model. A model error estimation value that estimates a model error of the flow sensor model with respect to the flow sensor and a flow error estimation value that estimates a flow error due to a change in the fluid control valve are obtained, and the model error estimation value is input into the flow sensor model. And outputs an estimated value to a feedback controller on the basis of the flow rate error estimated value and a valve flow rate estimated value output by the valve model.

The present invention provides a motor controller and its motor temperature sampling circuit. The motor temperature sampling circuit includes a main sampling branch and an auxiliary sampling branch. The input ends of both are connected to the output end of the motor temperature sensor, and the output ends of both are respectively connected to two input ends of a processor in the motor controller. Moreover, the main sampling branch includes a differential amplification link that the auxiliary sampling branch does not have. Therefore, the auxiliary sampling branch can respond to the change of the output voltage of the temperature sensor faster than the main sampling branch. The two output signals are compared in real time in the processor. When the motor temperature sensor has a disconnection, the difference between the two output signals is greater than a preset threshold, thereby realizing the rapid detection of the disconnection of the temperature sensor line. Moreover, the motor temperature sampling circuit can also automatically identify the sensor model by adjusting the voltage division parameters and the gain coefficient of the differential amplification link.

Method for evaluating a data-based sensor model (5) for determining a change-point time in a sensor signal time series (S), comprising the following steps: - Providing (S2) an evaluation signal time series (A) in an evaluation time window of a sensor signal time series (S); - Determining (S3) sensor signal segments (F1, F2, F3, F4) from the evaluation signal time series (A) that are temporally shifted or offset from each other by a number of sampling steps, wherein the sensor signal segments (F1, F2, F3, F4) have a shorter length than the evaluation signal time series; - Determining (S4) one or more frequency contributions from the sensor signal segments (F1, F2, F3, F4) using an FFT or a Goertzel algorithm; - Evaluating (S5, S6) the frequency contributions in a trained data-based sensor model (5) to determine a change-point time within the evaluation time window.

The application discloses a kind of scanning path planning methods, equipment, medium and product for aircraft upper surface, which comprises: based on curvature, normal vector and point cloud density joint detection algorithm, the overall point cloud data of the aircraft to be scanned is divided into data, to obtain each target point cloud partition corresponding to each target functional partition in the upper surface of the aircraft, after rectification treatment along the direction of normal vector, generate initial candidate viewpoint set and screen out safe viewpoint set.According to the visibility matrix constructed by the sensor model, the optimal viewpoint set is screened out from the safe viewpoint set using the greedy algorithm, and at least one optimal access sequence is solved using a hybrid solution strategy.The scanning path corresponding to each optimal access sequence is constructed and respectively issued to the unmanned aerial vehicle.The technical scheme can improve the detection efficiency and safety of the upper surface of the aircraft, and enhance the degree of automation and detection stability of the system.

Techniques related to temporary setpoint values are disclosed. The techniques may involve causing operation of a fluid delivery device in a closed-loop mode for automatically delivering fluid based on a difference between a first setpoint value and an analyte concentration value during operation of the fluid delivery device in the closed-loop mode. Additionally, the techniques may involve obtaining a second setpoint value. The second setpoint value may be a temporary setpoint value to be used for a period of time to regulate fluid delivery, and the second setpoint value may be greater than the first setpoint value. The techniques may further involve causing operation of the fluid delivery device for automatically reducing fluid delivery for the period of time based on the second setpoint value.

The invention discloses a method for processing signals of an absolute position sensor, which relates to the technical field of signal processing, and comprises the following steps of: constructing a simplified sensor model: constructing in a manner of dividing a rotation process according to the number of teeth of a sensor gear; similarity evaluation: the similarity evaluation is used for determining the similarity between the sensor and the model to obtain accurate position information, and comprises two parts of difference calculation and maximum similarity determination; the position calculation step comprises two aspects of memory use optimization and calculation complexity optimization; the method tolerance and precision evaluation is realized by calculating the similarity of signal values of the sensor at different angles; according to the method, the unique signal combination of each position of the sensor is focused, the list containing all possible output combinations and corresponding positions thereof is constructed, the actual position is determined by evaluating the similarity between the current measurement data of the sensor and the model, the problem that a traditional method is sensitive to the characteristic change of the sensor is solved, and the reliability and precision of position calculation are improved.

The present application discloses a supervision method for bridge maintenance operations, belonging to construction site management, including: respectively establishing a sensor model, a personnel model and a bridge BIM model; registering the sensor model and the bridge BIM model by using a geographic coordinate system; integrating the personnel model, the registered sensor model and the bridge BIM model through a GIS platform to obtain a digital twin scenario based on BIM+GIS; obtaining the GPS coordinate data of the operating personnel through an intelligent safety helmet and converting the GPS coordinate data into coordinate data in a Cartesian coordinate system; establishing a multi-source data correction mechanism to correct the coordinate data in the Cartesian coordinate system; and displaying the positions of the operating personnel in real time in the digital twin scenario through the personnel model according to the corrected coordinate data. Aiming at the low positioning accuracy of operating personnel caused by GPS signal drift in bridge maintenance, the present application improves the supervision positioning accuracy.

To make it possible to accurately control a flow rate while realizing a high-speed response.SOLUTION: An observer: obtains a model error estimation value obtained by estimating the model error of a flow rate sensor model with regard to a flow rate sensor, and a flow rate error estimation value obtained by estimating a flow rate error according to a change of a fluid control valve, on the basis of the deviation between a flow rate measurement value outputted from the flow rate sensor and a flow rate estimation value outputted from the flow rate sensor model; inputs the model error estimation value into the flow rate sensor model; and outputs the estimation values to a feedback controller on the basis of the flow rate error estimation value and the valve flow rate estimation value outputted from the valve model.SELECTED DRAWING: Figure 2

This invention provides a method, apparatus, and electronic device for modeling maritime rescue processes. The method includes: constructing a hierarchical model architecture driven by discrete event system specifications for the maritime rescue process, dividing the maritime rescue system model into a discrete event system layer and an object model layer; constructing a generalized rescue platform model and modular sub-models; the modular sub-models include a controller model, a maneuvering model, and a sensor model; constructing a complete rescue system simulation architecture based on the generalized rescue platform model; and performing repeated simulations and performance analysis on the complete rescue system simulation architecture to obtain a performance analysis report. This invention achieves accurate representation of the rescue system and solves the problems of poor accuracy and completeness in existing related technologies for rescue modeling.

The invention provides an alumen ustum detection dosing control method, system and equipment based on visual identification, and belongs to the technical field of water treatment. According to the method, water inlet parameters, water outlet parameters and alumen ustum images of a clarification tank are collected in real time, and alumen ustum characteristic parameters including the number of alumen ustum, the area proportion, the equivalent diameter, the circularity and the fractal dimension are extracted; performing time sequence alignment on the collected data to enable the related data and the alumen ustum characteristic parameters to correspond to the same batch of water flow, and associating with the dosing amount to obtain dosing time sequence data; inputting the dosing time sequence data into a pre-trained multi-layer sensor model to obtain a target dosing amount of next dosing; and the dosing flow is calculated according to the target dosing amount and the real-time concentration of the medicament, and the opening degree of the flow valve is controlled through a PID algorithm. By means of the technical scheme, intelligent association of alumen ustum characteristics and the dosing amount is achieved, and the accuracy and automation degree of dosing control in the water treatment process are improved.

The invention discloses an unmanned aerial vehicle license plate recognition simulation platform based on ROS and a construction method of the unmanned aerial vehicle license plate recognition simulation platform. The method comprises the steps of 1, building a scene, an unmanned aerial vehicle model, a sensor model and flight control; step 2, releasing the sensor data of the virtual scene through ROS; 3, a PX4 flight control instruction is converted into physical motion of the unmanned aerial vehicle model through a PX4-Gazebo simulation system, flight state data is obtained, and deployment and verification of a flight control algorithm are achieved; 4, completing the development and integration of a license plate recognition function, and obtaining recognition result data; and 5, realizing the display of the multi-dimensional data. According to the invention, the flight state and the recognition effect of the unmanned aerial vehicle during execution of the license plate recognition task can be simulated efficiently.

The invention relates to the technical field of Internet of Things data interaction, in particular to a data interaction method based on Internet of Things equipment acquisition, which comprises the following steps: S1, acquiring non-electric quantity data of a ring main unit by an Internet of Things sensor, and accessing the non-electric quantity data into a ring main unit DTU through a wireless receiving device via a Modbus protocol to realize software and hardware fusion; s2, the DTU carries out timestamp alignment and format standardization on the non-electric quantity data and electric quantity data collected by the DTU, generates an integrated data packet conforming to a DL / T634.5101 protocol and marks the priority; s3, performing hierarchical encryption on the data based on the priority; s4, the DTU uploads the encrypted data to the distribution automation master station according to the priority; s5, the master station decrypts and analyzes the data based on the data point table, and pushes the data to the dynamically expanded sensor model for correlation analysis; and S6, realizing intelligent study and judgment, health evaluation release and abnormity alarm of the ring main unit. The problems of heterogeneous access of data of the ring main unit, unbalanced transmission efficiency and safety, poor expansibility and the like are solved, and the intelligent operation and maintenance level of the power distribution network is improved.

The present disclosure generates an industrial machine program for generating a machining line representing an arbitrary and complex trajectory on a surface of a workpiece and for executing machining work along the trajectory based on the generated machining line.This programming device generates a program by which corresponding models for an industrial machine, a tool, a visual sensor, and a workpiece are arranged in a virtual space, and the industrial machine model performs machining on the workpiece model using the tool model, wherein: a label on which an outline is displayed is arranged on the surface of the workpiece model; a captured image of the label at a position where the visual model is arranged is generated based on image acquisition parameters set for the visual sensor model; an outline is detected by performing image processing on the captured image; a machining line is generated based on the detected outline; a normal vector to the surface of the workpiece model is calculated on the generated machining line; and the program is generated based on the machining line and the normal vector.

Embodiments of the present application disclose a reliability determination method and device for an automatic driving system, an equipment and a medium. The method comprises the following steps: constructing a vehicle model, a sensor model, a control model of the automatic driving system and a scene model, obtaining a scene simulation result of the automatic driving system, constructing a response surface of a scene variable and the scene simulation result based on the scene simulation result and each scene variable corresponding to the scene model, and determining a failure probability corresponding to the automatic driving system based on the response surface, a probability density function of each scene variable, a range of each scene variable and a preset failure condition. The method realizes quantification of a reliability result of the automatic driving system, provides strong support for development and improvement of the automatic driving system, and solves the technical problem that all test scenes cannot be enumerated in the prior art.

The invention discloses an unmanned ship multi-group dynamic coverage and collision avoidance system based on anisotropic perception. The unmanned ship multi-group dynamic coverage and collision avoidance system comprises a coverage control module, a safety control module and an unmanned ship module, the coverage control module comprises a sensing quantization module, a coverage level calculation module and a dynamic coverage sensing module; the safety control module comprises an intra-group collision avoidance module, a communication keeping module, an angle control module, an obstacle avoidance control module and a group collision avoidance module; the output end of the conical collision avoidance module is connected with the input end of the unmanned ship module; the input end of the unmanned ship module is connected with the output end of the dynamic coverage module, the output end of the conical collision avoidance module and the sideslip angular velocity, and the output end of the unmanned ship module is connected with the input end of the sensing quantification module and the input end of the safety control module. According to the method, a conical sensor model is constructed, a dynamic coverage method based on a perception quantification mechanism is designed, a path does not need to be planned again according to the actual sea condition, and real-time collaborative coverage among multiple unmanned ships is achieved.

The present disclosure relates to the field of bio-information technologies, in particular to a single-device multi-physiological-parameter synchronization and fusion method and a multi-device multi-physiological-parameter synchronization device. The single-device multi-physiological-parameter synchronization and fusion method includes the following steps: constructing a one-to-one wireless transmission system; constructing a one-to-many wired transmission system; initiating a synchronization instruction; checking information; matching information to align first collection data packets of the sensor modules. In the present disclosure, the master collection control module sends synchronization information to multiple sensor modules at the same time, and the master collection control module, based on multiple pieces of timestamp information, maps the collection data packets sent from the multiple sensor modules by time mapping, without requiring multiple sensor modules to be series-connected through a same clock source. In this case, the sensor models are not limited and real-time adjustment can be made to the sampling rate, ensuring high synchronization efficiency between multiple sensor modules.